Video data reduction with error resilience based on macroblock reorder

2005 ◽  
Vol 14 (1) ◽  
pp. 013008
Author(s):  
Tanzeem Muzaffar
Keyword(s):  
Data Reduction ◽  
Error Resilience ◽  
Video Data

scholarly journals VIDEO COPY DETECTION UTILIZING THE LOG-POLAR TRANSFORMATION

2016 ◽  
pp. 8-13
Author(s):  
Daniel Reynolds ◽  
Richard A. Messner
Keyword(s):  
Data Reduction ◽  
Reduction Process ◽  
Video Data ◽  
Video Frame ◽  
Detection Accuracy ◽  
Video Copy Detection ◽  
Copy Detection ◽  
Frame Size ◽  
Detection Process ◽  
Processing Step

Video copy detection is the process of comparing and analyzing videos to extract a measure of their similarity in order to determine if they are copies, modified versions, or completely different videos. With video frame sizes increasing rapidly, it is important to allow for a data reduction process to take place in order to achieve fast video comparisons. Further, detecting video streaming and storage of legal and illegal video data necessitates the fast and efficient implementation of video copy detection algorithms. In this paper some commonly used algorithms for video copy detection are implemented with the Log-Polar transformation being used as a pre-processing step to reduce the frame size prior to signature calculation. Two global based algorithms were chosen to validate the use of Log-Polar as an acceptable data reduction stage. The results of this research demonstrate that the addition of this pre-processing step significantly reduces the computation time of the overall video copy detection process while not significantly affecting the detection accuracy of the algorithm used for the detection process.

Color-Aware Packet Marking Based on H.264AVC Coder Extensions for Improved Video Quality

2014 ◽  
Vol 19 (2-3) ◽  
pp. 127-140
Author(s):  
Slawomir Przylucki
Keyword(s):  
Video Transmission ◽  
Video Quality ◽  
Error Resilience ◽  
Data Partitioning ◽  
Video Data ◽  
Technical Standards ◽  
Video Codecs ◽  
And Control ◽  
Packet Marking ◽  
Diffserv Architecture

Abstract In recent years there is a noticeable trend to implement the video transmission systems based on shared IP networks. At the same time new generations of video codecs such as H.264 are used in industrial installations. This situation forces the need for consideration of methods for efficient video transmission in industrial networks such as surveillance, identification and control systems. The first part of the article discusses the features of modern video codecs, relevant to the streaming applications. Attention is focused on the extensions of the H.264 standard that increase the error-resilience, particularly Data Partitioning (DP) and Flexible Macroblock Ordering (FMO). Next, the principles of prioritization of the video traffic based on the DiffServ architecture is discussed. In this context, separated section presents in detail the rules for packets marking which enable appropriate forwarding the video data. This information is referenced to current recommendations and technical standards. Next the performance of several classical packet marking algorithms and their possible modifications using FMO- and DP-based errorresilience configurations of H.264 are verified in simulations.

Reduction of Video License Plate Data

2002 ◽  
Vol 1804 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Rajeev Gupta ◽  
Jon D. Fricker ◽  
David P. Moffett
Keyword(s):  
Data Reduction ◽  
Video Processing ◽  
Character Recognition ◽  
Reduction Process ◽  
Data File ◽  
Video Data ◽  
License Plate ◽  
Small Areas ◽  
Electronic File ◽  
License Plate Readers

Video license plate surveys have been used for more than a decade in Indiana to help produce origin-destination tables in corridors and small areas. In video license plate surveys, license plate images are captured on videotape for data reduction at the analyst’s office. In most cases, the letters and numbers on a license plate are manually transcribed to a data file. This manual process is tedious, time-consuming, and expensive. Although automated license plate readers are being implemented with success elsewhere, their dependence on high-end equipment makes them too expensive for most applications in Indiana. Presented are the results of an attempt to use standard video cameras and tapes, readily available video processing equipment, and open-source software to minimize the human role in the data reduction process and thus reduce the expenses involved. The process of automatically transcribing video data can be divided into subprocesses. Analog video data are digitized and stored on a computer hard disk. The resulting digital images are further processed, by using image-processing algorithms, to locate and extract the license plate and time stamp information. Character recognition techniques can then be applied to read the license plate number into an electronic file for the desired analysis. The described video license plate data reduction (VLPDR) software can identify video frames that contain vehicles and discard the remaining frames. VLPDR can locate and read the time stamps in most of these frames. Although VLPDR cannot read the license plate numbers into a data file, this final step is made easier by a user-friendly graphical user interface. VLPDR saves a significant amount of manual data reduction. The amount of labor saved depends on the parameters chosen by the user.

The CLASSIC/CLIMB Data Reduction: The Math

2014 ◽  
Vol 69-70 ◽  
pp. 75-99
Author(s):  
T. ten Brummelaar
Keyword(s):  
Data Reduction

ELECTRONIC AREA DETECTOR DATA REDUCTION SYSTEM AT THE SRS

1986 ◽  
Vol 47 (C5) ◽  
pp. C5-109-C5-113
Author(s):  
J. W. CAMPBELL ◽  
D. CROFT ◽  
J. R. HELLIWELL ◽  
P. MACHIN ◽  
M. Z. PAPIZ ◽  
...  
Keyword(s):  
Data Reduction ◽  
Area Detector ◽  
Reduction System

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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